How AI-Based Systems Can Induce Reflections: The Case of AI-Augmented Diagnostic Work

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Abdel-Karim, Benjamin; Pfeuffer, Nicolas; Carl, K. Valerie; Hinz, Oliver

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelli ... Expand

Abstract: This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of MLbased decision support systems and also enriches theories on human-AI augmentation. Collapse

Topics: artificial intelligence decision making decision support system algorithmic transparency recommender system

Methods: cognitive walkthrough survey theory development qualitative interview Student's t-test

Theories: learning theory information systems theory theory of abduction

Gamified monetary reward designs: Offering certain versus chance-based rewards

2023 | Information Systems Journal | Citations: 0

Authors: Adam, Martin; Reinelt, Annika; Roethke, Konstantin

Abstract: To motivate visitors to engage with websites, e-tailers widely employ monetary r ... Expand

Abstract: To motivate visitors to engage with websites, e-tailers widely employ monetary rewards (e.g., vouchers, discounts) in their website designs. With advances in user interface technologies, many e-tailers have started to offer gamified monetary reward designs (MRDs), which require visitors to earn the monetary reward by playing a game, rather than simply claiming the reward. However, little is known about whether and why gamified MRDs engage visitors compared to their non-gamified counterpart. Even less is known about the effectiveness of gamified MRDs when providing certain or chance-based rewards, in that visitors do or do not know what reward they will gain for successfully performing in the game. Drawing on cognitive evaluation theory, we investigate gamified MRDs with certain or chance-based rewards and contrast them to non-gamified MRDs with certain rewards in user registration systems. Our results from a multi-method approach encompassing the complementary features of a randomised field experiment (N = 651) and a randomised online experiment (N = 330) demonstrate differential effects of the three investigated MRDs on user registration. Visitors encountering either type of gamified MRD are more likely to register than those encountering a non-gamified MRD. Moreover, gamified MRDs with chance-based rewards have the highest likelihood of user registrations. We also show that MRDs have distinct indirect effects on user registration via anticipated experiences of competence and sensation. Overall, the paper offers theoretical insights and practical guidance on how and why gamified MRDs are effective for e-tailers. Collapse

Topics: website electronic mail electronic commerce gamification website design

Methods: experiment randomized field experiment survey experimental task mixed method

Theories: cognitive evaluation theory self determination theory

How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

2023 | Information Systems Frontiers | Citations: 0

Authors: Aghakhani, Navid; Oh, Onook; Gregg, Dawn; Jain, Hemant

Abstract: This study extends our understanding of what makes an online review useful by ex ... Expand

Abstract: This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed. Collapse

Topics: online review user-generated content Python website data breach

Methods: topic model term frequency–inverse document frequency NLTK statistical hypothesis test robustness check

Theories: elaboration likelihood model

Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing

2023 | Information Systems Research | Citations: 0

Authors: Alashoor, Tawfiq; Keil, Mark; Smith, H. Jeff; McConnell, Allen R.

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the i ... Expand

Abstract: The confluence of digital transactions, growing cybersecurity threats, and the internet of the future (e.g., web 3.0 and the metaverse) have made information privacy increasingly important to consumers and companies that rely on consumers willingly sharing their personal information. Although information privacy has been of interest to researchers for decades and much has been learned, one thing that perplexes scholars is the privacy paradox, which we define as a mismatch between stated privacy concerns and actual disclosure behaviors. In this paper, we shed light on this phenomenon and show that low-effort information processing triggered by cognitive depletion (Experiment 1), positive mood (Experiment 2), or both (Experiment 3) significantly attenuates the association between stated privacy concerns and disclosure behaviors. These findings do not indicate that individuals do not care about privacy because we find consistent evidence in the three experiments for a significant negative association between stated privacy concerns and disclosure behaviors when individuals have sufficient cognitive capacity (Experiment 1), experience a negative (or neutral) mood (Experiment 2), or have sufficient cognitive capacity coupled with a negative mood state (Experiment 3). Our findings reveal that the paradox is neither an absolute phenomenon nor a myth, but its existence is conditional on contextual factors, including psychological factors related to information processing. We discuss our contribution to privacy theory and provide implications for consumers, companies, and policymakers. History: Alessandro Acquisti, Senior Editor; Idris Adjerid, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1182. Collapse

Topics: information privacy concern privacy decision making mobile application task productivity

Methods: experiment descriptive statistic statistical hypothesis test theory development Student's t-test

Theories: elaboration likelihood model

Perceived Algorithmic Control: Conceptualization and Scale Development

2023 | European Conference On Information Systems | Citations: 1

Authors: Alizadeh, Armin; Hirsch, Felix; Benlian, Alexander; Wiener, Martin; Cram, W.

Abstract: Organizations today leverage big data analytics combined with modern informatio ... Expand

Abstract: Organizations today leverage big data analytics combined with modern information and communication technology (ICT) to control workers in a largely automated manner. This phenomenon is commonly referred to as algorithmic control (AC). Research has shown that worker perceptions of AC applications can impact critical outcomes such as worker resistance, continuance intention, and well-being. Despite recent efforts to better understand these worker-level effects of AC, no systematic measures of workers' perceptions of AC exist to date. In this study, we develop and present a scale to empirically measure AC at the worker level. In particular, following established scale development guidelines, we conceptualize AC in terms of seven distinct forms, and derive, evaluate, and validate a final set of 33 measurement items. In doing so, our study paves the way for a much-needed, more systematic investigation of AC and its worker-level implications. Collapse

Topics: database system video conference social media

Methods: qualitative interview survey design psychometrics card sorting descriptive statistic

The Threatening Effect of Invoked Help from Highly Competent Intelligent Agents

2023 | International Conference on Information Systems | Citations: 0

Authors: Adam, Martin; Diebel, Christopher; Goutier, Marc

Abstract: Empowered with artificial intelligence, intelligent agents (IAs) increasingly o ... Expand

Abstract: Empowered with artificial intelligence, intelligent agents (IAs) increasingly offer help not only in response to user prompts (i.e., user-invoked help) but also without user prompts (i.e., IA-invoked help). Additionally, IAs are becoming more competent and even surpassing users in performing many computational and repetitive tasks. Drawing on self-affirmation theory, we investigate users' acceptance of IA-versus user-invoked help for identity-defining tasks from IAs with different levels of relative competence. We conducted an experiment with 199 software developers and found that IA-invoked (vs. user-invoked) help increases self-threat and thus reduces users' willingness to accept help from IAs. Moreover, relative competence moderates this effect, in that only IAs having relatively higher (vs. lower or equal) competence cause self-threat. Our study contributes to a better understanding of the self-threatening effects of IA-invoked (vs. user-invoked) help from IAs and the related role of relative competence that crucially shapes effective user-IA collaborations. Collapse

Topics: software developer user acceptance accounting artificial intelligence systems development

Methods: experiment business process modeling experimental task survey linear regression analysis

Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages

2023 | Information Systems Research | Citations: 0

Authors: Adam, Martin; Roethke, Konstantin; Benlian, Alexander

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs ... Expand

Abstract: Customers in sales processes increasingly encounter automated sales agents (ASAs) that complement or replace human sales agents (HSAs). Yet, little is known about whether, how, and why customers respond to ASAs in contrast to HSAs across successive decision stages of the same sales process. Even less is known about customer responses to HSA-ASA combinations, where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single sales agent. Against this backdrop, this paper explores the influence of increasingly common sales representative (rep) types (i.e., ASA, HSA, and HSA-ASA) on customer decisions across sales stages. Drawing on information processing theory and the literature on hedonic-utilitarian decision making, we investigate customer responses to text-based ASAs from vendor companies in two common early stages of email sales processes (i.e., sales initiation stages) when customers successively decide whether to indicate their initial interest in an offer and then, whether to provide their contact information. Specifically, we conducted two complementary multi-decision experiments, namely (1) a randomized field experiment in a high-stakes sales initiation setting (n = 325) and (2) a subsequent randomized online experiment to complement the real-world insights (n = 408). Our core findings reveal reversing effect patterns of sales rep types across stages: although customers are more likely to indicate their initial interest to HSAs (versus ASAs) because of HSAs’ higher levels of social presence, they are less likely to provide contact information to HSAs because of HSAs’ lower levels of performance expectancy and effort expectancy. We also show that HSA-ASA combinations can be reasonable options for single ASAs, yet contextual features of the sales setting may affect differential customer responses to HSA-ASA combinations (versus ASAs) in each sales stage. Taken together, we uncover shifting effect patterns in customer responses to sales rep types across successive sales stages and shed light on the consecutive underlying mechanisms that explain these shifts. These findings have significant implications for vendor companies seeking to allocate HSAs and/or ASAs effectively across various decision stages in sales processes and beyond. History: Wonseok Oh, Senior Editor and Khim Yong Goh; Associate Editor. Funding: This work was supported by the Center for Responsible Digitality (ZEVEDI) and the German Research Foundation (DFG) [Grant 471168026]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1171. Collapse

Topics: electronic mail social presence decision making recommender system information system use

Methods: experiment randomized field experiment regression analysis method experimental task field experiment

Theories: information processing theory

A Signal Detection Theory Approach for Predicting Immunity to Pandemic Vaccine Fake News

2023 | HICSS | Citations: 0

Authors: Barajas, J.; John, R.

Abstract: Managing a contagious disease pandemic, such as , requires that the public unde ... Expand

Abstract: Managing a contagious disease pandemic, such as , requires that the public understand and cooperate with behavioral guidelines to reduce viral transmission. This research uses Signal Detection Theory (SDT) to explore how U.S. adults distinguish between true and false information related to COVID-19 vaccines. A total of 372 U.S. adults categorized 17 true and 17 false COVID-19 vaccine headlines. Item Response Theory analyses suggest that the ability to identify true information about the pandemic is a construct that can be reliably measured with our novel methodology. Signal Detection Theory analyses indicate high accuracy (AUC = 0.861), with no bias favoring either true or false responses. Overall, U.S. adults correctly classified 7 of 10 true and 8 of 10 false headlines. Multiple regression analyses on individual performance metrics reveal substantially lower accuracy among conservatives and those with lower scores on a measure of Actively Open-minded Thinking. Implications and limitations of these findings within the pandemic news context are discussed. Collapse

Topics: misinformation fake news pandemic social media covid 19

Methods: machine learning survey multiple linear regression survey design regression analysis method

Theories: signal detection theory

The Interplay of Information Processing, Mindfulness, and Affective State in Phishing Detection

2023 | International Conference on Information Systems | Citations: 0

Authors: Bera, Debalina; Kim, Dan

Abstract: This study investigates the effect of individuals' information processing modes ... Expand

Abstract: This study investigates the effect of individuals' information processing modes on phishing email detection mechanisms and accuracy. Drawing on the heuristic-systematic model of processing, mindful decision-making, and affect-and-persuasion literature, this study examines the antecedents (i.e., heuristic, systematic processing, general and domain mindfulness, and affective state) and behavioral consequences (i.e., detection accuracy) in phishing identification. Using a scenario-based survey experiment this study shows mindfulness and affectivity navigate information processing mechanisms, thus influence phishing detection accuracy. It aims to contribute to information security literature by examining novel associations between general, domain mindfulness and its effect on online users' information processing strategy and phishing detection accuracy. Furthermore, this study contributes to phishing training and awareness by identifying the function of cognitive-affective (affective states, trait mindfulness) and cognitive-behavioral (domain mindfulness) factors on the choice of information processing modes and phishing detection accuracy. Finally, the affective states might help improve the efficacy of automatic filters. Collapse

Topics: phishing mindfulness electronic mail email phishing data security

Methods: experiment survey partial least squares regression machine learning laboratory experiment

Theories: protection motivation theory

“Dear CEO and Board”: How Activist Investors’ Confidence in Tone Influences Campaign Success

2023 | Organization Science | Citations: 0

Authors: Brauer, Matthias; Wiersema, Margarethe; Binder, Philipp

Abstract: Activist hedge funds represent the most potent form of financial activism. Yet w ... Expand

Abstract: Activist hedge funds represent the most potent form of financial activism. Yet we do not fully understand how these activist investors, despite holding only a small stake in target firms, are able to influence management and the board to acquiesce to their demands, especially given the large uncertainty that their demands will improve shareholder value. Building on impression management (IM) theory, we propose that activist investors who express their concerns and demands with high confidence in their letters to target firms are likely to be perceived as knowledgeable and competent by the firm’s other shareholders, thus influencing the response of the firm’s management and board. In support of this theoretical proposition, results of our empirical analysis of 475 U.S. activist campaigns against U.S. companies between 2007 and 2019 suggest that confidence in tone in an activist’s letter, as a form of self-promotion, is positively associated with campaign success. We also observe that the positive association between confidence in tone in activist letters and campaign success is less for activists with greater success in prior campaigns and in campaigns with multiple activists. Our paper contributes to financial activism research by showing that activists’ verbal impression management can serve as an effective influence tactic in their campaigns. Our study also contributes to the emerging research stream on verbal IM by introducing a language attribute, confidence in tone, that has not been studied in management research and is distinct from past constructs examined in verbal IM research. Collapse

Topics: corporate governance strategic management organizational productivity decision making database system

Methods: descriptive statistic qualitative content analysis theory development linguistic analysis regression analysis method

Theories: behavioral theory